An interpretable machine learning model for preoperative prediction of renal mass malignancy.

Journal: Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
Published Date:

Abstract

OBJECTIVE: To develop and validate a predictive model for distinguishing benign and malignant renal masses using machine learning (ML) algorithms.

Authors

  • Zuheng Wang
    Department of Urology, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China.
  • Xing He
    University of Florida, Gainesville, Florida, USA.
  • Hanyang Ou
    Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, No. 22, Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, China.
  • Xiao Li
    Department of Inner Mongolia Clinical Medicine College, Inner Mongolia Medical University, Hohhot, Inner Mongolia, China.
  • Chunmeng Wei
    Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China.
  • Rongbin Zhou
    Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-Constructed By the Province and Ministry, Guangxi Medical University, No. 22, Shuangyong Road, Qingxiu District, Nanning City, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China.
  • Zequn Su
    Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, No. 22, Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, China.
  • Junhao Mi
    Guangxi Key Laboratory for Genomic and Personalized Medicine, Center for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, University Engineering Research Center of Digital Medicine and Healthcare, Guangxi Medical University, Nanning, Guangxi, China.
  • Wenhao Lu
    School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, 639798, Singapore.
  • Fubo Wang
    Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China. wangbofengye@163.com.

Keywords

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